Protocol number 93-M-0170, NCT00001360 The Section on Functional Imaging Methods (SFIM) has advanced functional MRI (fMRI) methodology through development of processing and acquisition methods and research on the underlying mechanisms behind the fMRI signal. The ultimate goals are to gain a deeper understanding of the healthy human brain and to apply fMRI clinically on an individual basis. Moment-to-Moment Classification of Sustained Attention (David Jangraw) Towards developing approaches for fMRI of individuals, we have been developing a new project in which we study the sources and natural fluctuations of sustained attention, the act of focusing on a single task for an extended period of time. This area of study has implications for mental disorders in which sustained attention abilities are affected, such as depression, attention deficit disorder, and post-traumatic stress disorder. We have developed pilot studies in which we record behavioral, eye tracking, and other physiological data as subjects perform a reading task in the presence or absence of distracting stimuli. We use the data to build a machine learning classifier that can distinguish moments when a subject is maintaining attention on the task at hand from moments when their attention wanes. Future work will use the classifier output as an objective moment-to-moment readout of sustained attention in an fMRI scanner, facilitating clearer pictures of the neural processes involved and potentially developing real-time interventions to improve sustained attention in education. Correlations Between EEG and fMRI Activity After ME-ICA Denoising (Jen nifer Evans) The technique of Multi-Echo ICA-based denoising (ME-ICA) has been used to reduce the influence of non-BOLD noise, including slow scanner drifts in the fMRI signal, enhancing the ability to study slow changes in neural activity that overlap with slow drifts, such as engagement and time-on-task effects. The contrast of a checkerboard was very slowly varied to create a slowly changing signal in both BOLD data and EEG evoked potential envelope. We separated the slow induced BOLD changes from slow non-BOLD drifts in the fMRI data using multi-echo fMRI and ME-ICA denoising. We then investigated the correspondence of the denoised data with the neural response. We found strong agreement between modalities, demonstrating the ability of ME-ICA denoising to reveal slow on the order of minutes - neural fluctuations while excluding the effects of scanner drift. Effect of Theta Burst TMS on Resting Connectivity Using Multi-Echo fMRI (Dan iel Handwerker, David Pitcher - LBC) We used multi-echo fMRI and ME-ICA denoising to examine the effect of theta burst transcranial magnetic stimulation (TBS) to the right superior temporal sulcus vs a control region, the right hand knob. When TBS was directed at the right parietal superior temporal sulcus (rpSTS), there was a decrease in connectivity between the rpSTS to the fusiform, middle occipital, and middle temporal gyri. These results show that TBS to the rpSTS selectively decreases functional connections to face-selective regions even without task-evoked responses. The preliminary results will be presented at the Society for Neuroscience Annual Meeting in October 2015. Quantitation of Gains in Contrast to Noise Ratio and Stability of ME-EPI (Ben Gutierrez, Dan Handwerker). We collected 103 five-minute multi-echo runs from two individuals while they performed a visuomotor task with a letter/number discrimination component and then characterized the improvement data quality and spatial reliability of activation maps by ME-EPI relative to standard approaches. We also demonstrated how that more gray matter voxels become reliably activated with considerably less data when using multi-echo EPI acquisition. These results were presented at the Organization for Human Brain Mapping Annual Meeting in June 2015. BOLD Connectivity Dynamics and its Relationship to Mental States (Javier Gonzalez-Castillo) In this study, we demonstrate a direct relationship between ongoing cognition and dynamic changes in whole-brain patterns of connectivity at the scale of tens of seconds. In a recent report we show how it is possible to accurately track on-going cognition (as imposed by task) at the individual level using BOLD connectivity patterns computed from data traces corresponding to time windows as short as 22.5 s econds. Tracking accuracy dropped markedly for subjects with the lowest task performance, confirming the relationship to ongoing cognition. Finally we also demonstrate that restricting connectivity patterns to a select group of ROIs decreases accuracy, emphasizing the distributed nature of cognitively meaning changes in dynamic connectivity. Evaluation of ME-ICA for Removal of T1 Baseline Shifts in Cardiac Gated fMRI (Javier Gonzalez-Castillo, Laura Buchanan) Imaging the brainstem with BOLD fMRI is difficult due to the pulsatile motion of blood flowing through large vasculature surrounding this neuronal structure. One solution is to trigger fMRI acquisitions in synchrony with the cardiac cycle, to ensure the brainstem is imaged always in the same position (cardiac-gated fMRI). Yet, the non-constant TR associated with cardiac-gated fMRI leads to artifactual baseline signal shifts (of a T1 origin) that need to be properly accounted for. ME-ICA has the potential to correct for T1 related artifacts (such as those present in cardiac-gated fMRI) in a completely data driven approach. In this study, we used an auditory block design paradigm to activate inferior colliculus (a small structure in the back of the brainstem part of the ascending auditory pathway). ME-ICA is able to reliably capture and remove signal baseline associated with variable TRs. Results also demonstrate improvements in detectability of BOLD activation in the inferior colliculus as compared with single-echo fMRI combined with model-driven removal. These results highlight the sensitivity of ME-ICA to T1-related artifacts of a different origin to those tested in other studies from our group, and demonstrate its feasibility for improving fMRI-BOLD studies focused in brainstem structures. Change in Hippocampal Myelination with Aerobic Exercise (Adam Thomas) In collaboration with Dr. Heidi Johansen-Berg at Oxford University, we have demonstrated volume growth in the anterior hippocampus after just six weeks of aerobic exercise. Previous rodent studies have shown the hippocampus to be a site of neurogenesis, which is facilitated by exercise. We went on to use nine different neuroimaging measures to characterize the change in anterior hippocampus. Using advanced statistical methods designed by Dr. Thomas Nichols at the University of Warwick, we were able to demonstrate that the change is dominated by myelination and not by grey matter or vasculature as other groups have predicted. This work has just received a favorable review at NeuroImage and will be published shortly.